ViCloD, an interactive web tool for visualizing B cell repertoires and analyzing intraclonal diversities: application to human B-cell tumors.

High throughput sequencing of adaptive immune receptor repertoire (AIRR-seq) has provided numerous human immunoglobulin (IG) sequences allowing specific B cell receptor (BCR) studies such as the antigen-driven evolution of antibodies (soluble forms of the membrane-bound IG part of the BCR). AIRR-seq data allows researchers to examine intraclonal differences caused primarily by somatic hypermutations in IG genes and affinity maturation. Exploring this essential adaptive immunity process could help elucidate the generation of antibodies with high affinity or broadly neutralizing activities. Retracing their evolutionary history could also clarify how vaccines or pathogen exposition drive the humoral immune response, and unravel the clonal architecture of B cell tumors. Computational methods are necessary for large-scale analysis of AIRR-seq properties. However, there is no efficient and interactive tool for analyzing intraclonal diversity, permitting users to explore adaptive immune receptor repertoires in biological and clinical applications. Here we present ViCloD, a web server for large-scale visual analysis of repertoire clonality and intraclonal diversity. ViCloD uses preprocessed data in the format defined by the Adaptive Immune Receptor Repertoire (AIRR) Community. Then, it performs clonal grouping and evolutionary analyses, producing a collection of useful plots for clonal lineage inspection. The web server presents diverse functionalities, including repertoire navigation, clonal abundance analysis, and intraclonal evolutionary tree reconstruction. Users can download the analyzed data in different table formats and save the generated plots as images. ViCloD is a simple, versatile, and user-friendly tool that can help researchers and clinicians to analyze B cell intraclonal diversity. Moreover, its pipeline is optimized to process hundreds of thousands of sequences within a few minutes, allowing an efficient investigation of large and complex repertoires.

Mechanisms of action of monoclonal antibodies in oncology integrated in IMGT/mAb-DB.

Background: Cancer cells activate different immune checkpoint (IC) pathways in order to evade immunosurveillance. Immunotherapies involving ICs either block or stimulate these pathways and enhance the efficiency of the immune system to recognize and attack cancer cells. In this way, the development of monoclonal antibodies (mAbs) targeting ICs has significant success in cancer treatment. Recently, a systematic description of the mechanisms of action (MOA) of the mAbs has been introduced in IMGT/mAb-DB, the IMGT® database dedicated to mAbs for therapeutic applications. The characterization of these antibodies provides a comprehensive understanding of how mAbs work in cancer. Methods: In depth biocuration taking advantage of the abundant literature data as well as amino acid sequence analyses from mAbs managed in IMGT/2Dstructure-DB, the IMGT® protein database, allowed to define a standardized and consistent description of the MOA of mAbs targeting immune checkpoints in cancer therapy. Results: A fine description and a standardized graphical representation of the MOA of selected mAbs are integrated within IMGT/mAb-DB highlighting two main mechanisms in cancer immunotherapy, either Blocking or Agonist. In both cases, the mAbs enhance cytotoxic T lymphocyte (CTL)-mediated anti-tumor immune response (Immunostimulant effect) against tumor cells. On the one hand, mAbs targeting co-inhibitory receptors may have a functional Fc region to increase anti-tumor activity by effector properties that deplete Treg cells (Fc-effector function effect) or may have limited FcγR binding to prevent Teff cells depletion and reduce adverse events. On the other hand, agonist mAbs targeting co-stimulatory receptors may bind to FcγRs, resulting in antibody crosslinking (FcγR crosslinking effect) and substantial agonism. Conclusion: In IMGT/mAb-DB, mAbs for cancer therapy are characterized by their chains, domains and sequence and by several therapeutic metadata, including their MOA. MOAs were recently included as a search criterion to query the database. IMGT® is continuing standardized work to describe the MOA of mAbs targeting additional immune checkpoints and novel molecules in cancer therapy, as well as expanding this study to other clinical domains.

A multi-objective based clustering for inferring BCR clonal lineages from high-throughput B cell repertoire data.

The adaptive B cell response is driven by the expansion, somatic hypermutation, and selection of B cell clonal lineages. A high number of clonal lineages in a B cell population indicates a highly diverse repertoire, while clonal size distribution and sequence diversity reflect antigen selective pressure. Identifying clonal lineages is fundamental to many repertoire studies, including repertoire comparisons, clonal tracking, and statistical analysis. Several methods have been developed to group sequences from high-throughput B cell repertoire data. Current methods use clustering algorithms to group clonally-related sequences based on their similarities or distances. Such approaches create groups by optimizing a single objective that typically minimizes intra-clonal distances. However, optimizing several objective functions can be advantageous and boost the algorithm convergence rate. Here we propose MobiLLe, a new method based on multi-objective clustering. Our approach requires V(D)J annotations to obtain the initial groups and iteratively applies two objective functions that optimize cohesion and separation within clonal lineages simultaneously. We show that our method greatly improves clonal lineage grouping on simulated benchmarks with varied mutation rates compared to other tools. When applied to experimental repertoires generated from high-throughput sequencing, its clustering results are comparable to the most performing tools and can reproduce the results of previous publications. The method based on multi-objective clustering can accurately identify clonally-related antibody sequences and presents the lowest running time among state-of-art tools. All these features constitute an attractive option for repertoire analysis, particularly in the clinical context. MobiLLe can potentially help unravel the mechanisms involved in developing and evolving B cell malignancies.